MHHS Scoring

library(tableone)

Warning: package ‘tableone’ was built under R version 4.2.3

# Note that there's a package that does this smoothly. Need to add it as an alternative at some point

#columnNames<-read.csv("../01-RawData/MHHS-1_12202023.csv",header=FALSE)[1,]

#qual_data<-read.csv("../01-RawData/MHHS-1_12202023.csv",header=FALSE,col.names = columnNames)[-c(1:3),]

# write a new version that can be reimported with the proper column classes
#write.csv(qual_data,"../04-ProcessedData/MHHS-1_12202023_cleanheader.csv",row.names=FALSE)

Metadata and attention checks

AC1 - We care about the quality of our survey data. For us to get the most accurate measures of your opinions, it is important that you provide thoughtful answers to each question in this survey. Do you commit to providing thoughtful answers to the questions in this survey? (1=yes)

Notes that AC2 is wonky in Qualtrics. Response options are coded: 1 = strongly disagree 2 = Disagree 5 = Neither agree nor disagree 6 = Agree 2 = Strongly Agree

AC2 - These surveys can be very long and a little tiring. We want to ensure that you are still paying attention because we care about the quality of the data. If you are still paying attention and ready to move on, please click on the answer Agree. Consider whether you want to know if participants closely read the question and followed instructions, or where they perhaps answering honestly

AC3 - AC3 - Based on the text you read above, what color have you been instructed to enter? (correct answer is 3-green)

AC-4 - We care about the quality of our survey data. Knowing you will receive credit for completing this survey regardless of your answer, please answer the following question: Should we use your data? (1=yes)

save(mhhs_demotbl,"../04-ProcessedData/Demotable.RDS")

Error in save(mhhs_demotbl, "../04-ProcessedData/Demotable.RDS") : 
  object ‘../04-ProcessedData/Demotable.RDS’ not found

PROMIS_Global v1.2

To score the PROMIS_Global: First check if survey coded Excellent as 1 or as 5. Qualtrics will often reverse them. Higher scores should reflect better functioning.

PROMIS_Global1_1:PROMIS_Global1_7: Items rated from 5 (excellent) to 1 (Not at all) on original scale; PROMIS_Global1_1:PROMIS_Global1_7 are reversed from Qualtrics 1-5 defaults

PROMIS_Global2_1: emotional problems; past 7 days; 5 (never) to 1 (always); (global 10) PROMIS_Global2_2: fatigue; past 7 days (global 08)

PROMIS_Global3_1: pain. 5=0, 4=1:3, 3=4:6, 2=7:9, 1=10

For scoring, “2 questions were excluded from the calculation/evaluation as a result of the questionnaire development study: Global01 because of its statistical congruence with Global03. Global09r because of its approximately equal statistical correlation to physical and mental health.”

The Global Physical Health score is generated by summing responses to Global03 (PROMIS_Global1_3), Global06 (PROMIS_Global1_7), Global07rescored (PROMIS_Global3_1), and Global08rescored (PROMIS_Global2_2). The Global Mental Health score is generated by summing responses to Global02 (PROMIS_Global1_2), Global04 (PROMIS_Global1_4), Global05(PROMIS_Global1_5), and Global10rescored (PROMIS_Global2_1)

promis_global<-mhhs_data %>% select(contains("PROMIS"))

#score items so that higher scores reflect better functioning 

# pain item: 5=0, 4=1:3, 3=4:6, 2=7:9, 1=10

# renaming the Promis to match the original measure

promis_global<-promis_global %>% rename_with(~sprintf("PROMIS_Global0%d",1:5),.cols=c(PROMIS_Global1_1:PROMIS_Global1_5)) %>%  rename(PROMIS_Global09=PROMIS_Global1_6,PROMIS_Global06=PROMIS_Global1_7, PROMIS_Global10=PROMIS_Global2_1, PROMIS_Global08=PROMIS_Global2_2,PROMIS_Global07=PROMIS_Global3_1)%>% mutate(across(c(PROMIS_Global01:PROMIS_Global08),~6-.)) %>% 
  mutate(PROMIS_Global07=recode(PROMIS_Global07,`0`=5,`1`=4,`2`=4,`3`=4,`4`=3,`5`=3,`6`=3,`7`=2,`8`=2,`9`=2,`10`=1))%>% 
  rowwise() %>%  mutate(PG_PhysHealth=sum(c(PROMIS_Global03,PROMIS_Global06,PROMIS_Global07,PROMIS_Global08)),PG_MentHealth=sum(c(PROMIS_Global02,PROMIS_Global04,PROMIS_Global05,PROMIS_Global10))) %>% ungroup()

promis_global_scoring<-promis_global %>% select(-PROMIS_Global01, -PROMIS_Global09,-PG_PhysHealth,-PG_MentHealth)

key_promis.list<-list(PH=c("PROMIS_Global03","PROMIS_Global06","PROMIS_Global07","PROMIS_Global08"),MH=c("PROMIS_Global02","PROMIS_Global04","PROMIS_Global05","PROMIS_Global10"))

key_promis<-make.keys(nvars=8,key_promis.list,item.labels = colnames(promis_global_scoring))
scales_promis<-scoreItems(key_promis,promis_global_scoring)
summary(scales_promis)

GHSQL

GHSQL has one open-response item. This is not included in the summary score. Scoring also seems somewhat flexible and includes informal/formal; personal-emotional/suicide; help from anyone/Help from no-one.

Range 1-7 (Extremely unlikely to Extremely Likely)

ghsql<-mhhs_data %>% select(contains("GHSQL"))

ghsql<-ghsql %>% mutate(General=rowSums((select(ghsql,GHSQL_Pers_1:GHSQL_Pers_9,GHSQL_SUI_1:GHSQL_SUI_9)))) %>% 
 rowwise() %>%  
  mutate(Personal=sum(c_across(GHSQL_Pers_1:GHSQL_Pers_8)),
         Suicide=sum(c_across(GHSQL_SUI_1:GHSQL_SUI_8)),
         Informal_all=sum(c(GHSQL_Pers_1,GHSQL_SUI_1,GHSQL_Pers_2,GHSQL_SUI_2,GHSQL_Pers_3,GHSQL_SUI_3,GHSQL_Pers_4,GHSQL_SUI_4)),
         Formal_all=sum(c(GHSQL_Pers_5,GHSQL_SUI_5,GHSQL_Pers_6,GHSQL_SUI_6,GHSQL_Pers_7,GHSQL_SUI_7,GHSQL_Pers_8,GHSQL_SUI_8)),
         None_all=sum(c(GHSQL_Pers_9,GHSQL_SUI_9))) %>% 
  ungroup() %>% #remove rowwise grouping 
  mutate(Personal_z=scale(Personal),Suicide_z=scale(Suicide),Informal_all_z=scale(Informal_all),Formal_all_z=scale(Formal_all),None_all_z=scale(None_all))

#make a dataframe with only the items to be scored/that need reliability
ghsql_scoring<-ghsql %>% dplyr::select(1:9,12:20)
key_ghsql.list<-list(General=c(1:18),Personal=c(1:9),Suicide=c(10:18),Formal=c(5,14,6,15,7,16,8,17),Informal=c(1,10,2,11,3,12,4,13))
key_ghsql<-make.keys(18,key_ghsql.list,colnames(ghsql_scoring))

scales_ghsql<-scoreItems(key_ghsql.list,ghsql_scoring)
summary(scales_ghsql)

Call: scoreItems(keys = key_ghsql.list, items = ghsql_scoring)

Scale intercorrelations corrected for attenuation 
 raw correlations below the diagonal, (unstandardized) alpha on the diagonal 
 corrected correlations above the diagonal:
         General Personal Suicide Formal Informal
General     0.78     1.36    1.24   1.07     1.05
Personal    0.87     0.52    1.03   1.16     1.06
Suicide     0.93     0.63    0.72   1.03     1.05
Formal      0.86     0.76    0.80   0.83     0.59
Informal    0.82     0.68    0.79   0.48     0.78

#histogram to explore the range 
ghsql %>% select(GHSQL_Pers_1:GHSQL_Pers_9) %>% gather() %>% ggplot(aes(y=value,x=key)) + geom_boxplot() + scale_x_discrete(labels=c("Intimate partner", "Friend","Parent","Other relative","MHP","Phone helpline","Doctor","Minister","Not seek help"))+ theme(axis.text.x = element_text(angle=90))

Warning: Removed 300 rows containing non-finite values (`stat_boxplot()`).

ghsql %>% select(GHSQL_SUI_1:GHSQL_SUI_9) %>% gather() %>% ggplot(aes(y=value,x=key)) + geom_boxplot() + scale_x_discrete(labels=c("Intimate partner", "Friend","Parent","Other relative","MHP","Phone helpline","Doctor","Minister","Not seek help"))+ theme(axis.text.x = element_text(angle=90))

Warning: Removed 343 rows containing non-finite values (`stat_boxplot()`).

ghsql %>% select(Informal_all_z,Formal_all_z,None_all_z) %>% gather() %>% ggplot(aes(y=value,x=key)) + geom_boxplot() + scale_x_discrete(labels=c("Informal (Pers + Sui)","Formal (Pers + Sui)","None (Pers + Sui)"))+ theme(axis.text.x = element_text(angle=90))

Warning: attributes are not identical across measure variables; they will be dropped
Warning: Removed 178 rows containing non-finite values (`stat_boxplot()`).

ghsql %>% select(GHSQL_Pers_10_TEXT,GHSQL_SUI_10_TEXT) %>% filter(GHSQL_Pers_10_TEXT!=""|GHSQL_SUI_10_TEXT!="")

save(ghsql,file="../04-ProcessedData/ghsql.RDS")

DASS21

Stress items are 1, 6, 8, 11, 12, 14, 18 Anxiety items are 2, 4, 7, 9, 15, 19, 20,
Depression items are 3, 5, 10, 13, 16, 17, 21

The depression scale assesses dysphoria, hopelessness, devaluation of life, self-deprecation, lack of interest / involvement, anhedonia and inertia.

The anxiety scale assesses autonomic arousal, skeletal muscle effects, situational anxiety, and subjective experience of anxious affect. The stress scale is sensitive to levels of chronic nonspecific arousal. It assesses difficulty relaxing, nervous arousal, and being easily upset / agitated, irritable / over-reactive and impatient.

Scores for depression, anxiety and stress are calculated by summing the scores for the relevant items

Scores on the DASS-21 will need to be multiplied by 2 to calculate the final score and stay equivalent with DASS-42.

ISMI29

29-item measure assessing self stigma of mental illness. Includes subscales: alienation, stereotype endorsement, discrimination experience, social withdrawal, stigma resistance. Scored from 1-4. The entire Stigma resistance subscale is reverse scored.

Traditional scoring is dichotomous and uses means.

ismi<-mhhs_data %>% select(contains("ISMI"))

ismi<-ismi %>% rowwise() %>%  
  mutate(ISMI_Alientation=sum(ISMI29_1,ISMI29_5,ISMI29_8,ISMI29_16,ISMI29_17,ISMI29_21),
         ISMI_Stere=sum(ISMI29_2, ISMI29_6,ISMI29_10,ISMI29_18,ISMI29_19,ISMI29_23,ISMI29_29),
         ISMI_Discr=sum(ISMI29_3,ISMI29_15,ISMI29_22,ISMI29_25,ISMI29_28),
    ISMI_Withdrawal=sum(ISMI29_4,ISMI29_9,ISMI29_11,ISMI29_12,ISMI29_13,ISMI29_20)) %>% 
      mutate(ISMI29_7=recode(ISMI29_7,`1`=4,`2`=3,`3`=2,`4`=1),
            ISMI29_14=recode(ISMI29_14,`1`=4,`2`=3,`3`=2,`4`=1),
         ISMI29_24=recode(ISMI29_24,`1`=4,`2`=3,`3`=2,`4`=1),
          ISMI29_26=recode(ISMI29_26,`1`=4,`2`=3,`3`=2,`4`=1),
         ISMI29_27=recode(ISMI29_27,`1`=4,`2`=3,`3`=2,`4`=1)) %>%  
      mutate(ISMI_Stigmar=sum(ISMI29_7,ISMI29_14,ISMI29_24,ISMI29_26,ISMI29_27)) %>% 
  ungroup()


#make a dataframe with only the items to be scored/that need reliability
ismi_scoring<-ismi %>% dplyr::select(ISMI29_1:ISMI29_29)
key_ismi.list<-list(Alien=c(1,5,8,16,17,21),Stereotype=c(2,6,10,18,19,23,29),Discrimination=c(3,15,22,25,28),Withdrawal=c(4,9,11,12,13,20),StigmaR=c(7,14,24,26,27))

key_ismi<-make.keys(29,key_ismi.list,colnames(ismi_scoring))

scales_ismi<-scoreItems(key_ismi.list,ismi_scoring)
summary(scales_ismi)

Call: scoreItems(keys = key_ismi.list, items = ismi_scoring)

Scale intercorrelations corrected for attenuation 
 raw correlations below the diagonal, (unstandardized) alpha on the diagonal 
 corrected correlations above the diagonal:
               Alien Stereotype Discrimination Withdrawal StigmaR
Alien           0.81       1.05           1.06       1.07    1.04
Stereotype      0.85       0.80           1.16       1.04    1.30
Discrimination  0.68       0.74           0.51       1.05    1.70
Withdrawal      0.87       0.84           0.67       0.81    1.18
StigmaR         0.32       0.39           0.41       0.36    0.12

ismi$ISMI29_14

  [1] NA NA  2  2  2  4  2  3  1  2  2  2  2  4  2  2  1  1  1  3  2  3  4  2
 [25]  4  3  4  2  2  2  2  2  2  2  2  1  4  2  3  4  2  3  2  4  3  4  3  2
 [49]  3  1  3  2  2  3  4  2  4  2  2  2  2  2  2  2  3  1  2  2  4  2  4  3
 [73]  2  1  4  3  3  3  2  4  4  1 NA  2  2  2  3  3  3  4  2  2  3  1  2  2
 [97]  4  2  4  3  2  1  2  4  4  2  2  3  2  4  2  1  3  2  4  3  2  4  1  2
[121]  3  3  3  2  1  2  2  1  2  3  2  1  3  3  4  2  1  3  2  2  2  2 NA  4
[145] NA  2  3  2 NA NA  3  1  2  2  2  2  3  1  2  2  2  2  2  3  2  2  4  1
[169]  2  3  4 NA  2  2  2  2  3  2  4  4  2  2  3 NA  2  2  3  3  2  2  1  1
[193]  4  2  2  1  3  1  4  3  2  2  2  4  3  2  4 NA  4 NA  4  3  2  4  2  4
[217]  3  3  2  2  1  2  3  1  3  1  1  2  2  3  4  3  1  2  3  1  2  1  4  2
[241]  1  4  4  2  4  4  1  2  2  3  2  4  4  3  2  2  3  4  3  4  2  2  2  4
[265]  2  4  4  2  2  1  2  4  3  3  3  4 NA  1  1  1  3  4  4  1  1  2  4  4
[289]  2  2  2 NA  2  2  2  1  2  2  4  1  2  1  3  3  3  2  2  4  2  1  2  3
[313]  3  2  1  3  4  4  3  4  2  4  1  2  2 NA NA  4  1  2  3 NA  1  3  2  3
[337]  1  1  3  2  3  3  2  2  2  2  2  1  2  4  1  2  4  4  3  3  1  1  3  2
[361]  1  2  2  1  3  1  1  3  2  3  2 NA NA  2  1  2  2  4  1  4  1  3  2  2
[385]  4  4  2  4  1  1  4  2  1  2  2  1  2  2  4  4  2  2  1  1  3  2  3  4
[409]  2 NA  1 NA  2  2  2  4  2  1  2  4  1  3  2  1  2  3  4  2  2  2  1 NA
[433]  2 NA  3  2  2  2  2 NA  1  3  1  2  2  2  2  1  4  2  3 NA  4  1 NA  1
[457]  4  2  4  2  2  2  2  2  4  4  2  2  3  2  3  2  2  3  1  2  1  2  2  3
[481]  4  2 NA  1  2 NA  4  4  3  4  2 NA  4  3  2  2  2  3  1  2  2  2  4  1
[505]  2  2  3  4  2 NA NA  1  3  2  2 NA  3  2  4  4 NA  1  1  4  4  4  4 NA
[529]  3  1  1  3  2 NA  2  4  3  3  1 NA  2  2  2  2  4  2  2  1  2  2 NA  2
[553]  2 NA NA  1  2  2  2  2  2  2  2  4 NA  2  2  3  2 NA  3  2  1  4 NA  3
[577]  3  1  3  2  2  2  2  2  2  4  2  2  2  2  4  3  2  2  2  1  2  2  2  3
[601]  2  1  2  2  1  3  1  3  2  4  2  1  2  3  2  1 NA  4  4  3  4  4  3  2
[625]  3  2  2  2  1  2  3  4  2  2  2  4  4  3  2  3  4  1  4  2  3  1  2  3
[649]  3  2  3  3  2  2  1  1  1  1  3  1  4  2  3  2  1  1  1  2  4  4  3  2
[673] NA  4  2  1  2  3  2  1  3  4  1  3  4 NA  2  4  2  1  2  3  4  1  4  2
[697]  2  4  2  1  1  2  4  3 NA  1  2  3  1  2  3  2  2 NA  3  2  2  2  2  2
[721]  4  3  3  2  2  2  2  4  1  1  2  1  4  2  1  1 NA  1  3  2  1  3  3  2
[745]  2  2  3  2  1  3  2  3  4  2  1  2  1  1  2  2  2  2  2  3  2  2  4  2
[769]  4  2  2 NA NA  2  3  2  2 NA  1  3  4  4  3  4  2 NA  2  2  3  1  1  2
[793] NA  2  1  2  1 NA  1  2  2  2  2  4  4  2  2  2  2  3  2  3  2  1  2  2
[817]  2  2  3  3  3  3  3  1  2  2  1  4  3  2 NA  3  3  4  1  4  2  1  4  2
[841]  1  3 NA  2  1  2  4  2  2  4  3  2  3  2  3  3  4  2  4  2  1  1  1  3
[865]  3  3  3  2  2  2  1  1  1  2 NA  2  1  3  3 NA  1  2  3 NA NA NA  2

cor(ismi$ISMI_Stigmar,ismi$ISMI_Alientation,use="complete.obs")

[1] -0.1243007

ismi %>% select(ISMI_Alientation,ISMI_Stere,ISMI_Discr,ISMI_Withdrawal,ISMI_Stigmar) %>% gather() %>%  ggplot(aes(y=value,x=key,color=value))+geom_boxplot()+geom_jitter()

Warning: Removed 367 rows containing non-finite values (`stat_boxplot()`).
Warning: The following aesthetics were dropped during statistical transformation:
colour
ℹ This can happen when ggplot fails to infer the correct grouping structure in
  the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
  variable into a factor?
Warning: Removed 367 rows containing missing values (`geom_point()`).

For some reason, the rescored stigma resistance is a negative correlation

Distress Disclosure Index

Reverse score items 2, 4, 5, 8, 9, 10. Then sum the 12 items. Higher scores indicate a higher tendency to disclose distress, lower scores indicate greater concealment of distress.

ddi<-mhhs_data %>% select(contains("DDI"))

ddi<-ddi %>% mutate(across(c(DDI_2,DDI_4,DDI_5,DDI_8,DDI_9,DDI_10),~6-.)) %>%
  rowwise() %>% mutate(DDI_Total=sum(c_across(DDI_1:DDI_12))) %>% 
  ungroup()

ddi_scoring<-ddi %>% dplyr::select(DDI_1:DDI_12)

key_ddi.list<-list(Total=c(1:12))
key_ddi<-make.keys(12,key_ddi.list,colnames(ddi_scoring))

scales_ddi<-scoreItems(key_ddi.list,ddi_scoring)
summary(scales_ddi)

Call: scoreItems(keys = key_ddi.list, items = ddi_scoring)

Scale intercorrelations corrected for attenuation 
 raw correlations below the diagonal, (unstandardized) alpha on the diagonal 
 corrected correlations above the diagonal:
      Total
Total  0.91

ddi %>% select(DDI_Total) %>% gather() %>%  ggplot(aes(y=value,x=key,color=value))+geom_boxplot()+geom_jitter()

Warning: Removed 78 rows containing non-finite values (`stat_boxplot()`).
Warning: The following aesthetics were dropped during statistical transformation:
colour
ℹ This can happen when ggplot fails to infer the correct grouping structure in
  the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
  variable into a factor?
Warning: Removed 78 rows containing missing values (`geom_point()`).

ddi %>% select(DDI_Total) %>% add_column(ghsql %>% select(Personal,Suicide,General,None_all)) %>% cor.plot(.)

# checking the correlations ot ensure they make sense
cor.test(ddi$DDI_Total,ghsql$General)

    Pearson's product-moment correlation

data:  ddi$DDI_Total and ghsql$General
t = 8.7323, df = 746, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.2380376 0.3681729
sample estimates:
      cor 
0.3045256 

cor.test(ddi$DDI_Total,ghsql$Personal)

    Pearson's product-moment correlation

data:  ddi$DDI_Total and ghsql$Personal
t = 11.438, df = 780, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.3173029 0.4374647
sample estimates:
      cor 
0.3789801 

cor.test(ddi$DDI_Total,ghsql$Suicide)

    Pearson's product-moment correlation

data:  ddi$DDI_Total and ghsql$Suicide
t = 9.1746, df = 777, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.2478383 0.3746535
sample estimates:
      cor 
0.3126384 

cor.test(ddi$DDI_Total,ghsql$None_all)

    Pearson's product-moment correlation

data:  ddi$DDI_Total and ghsql$None_all
t = -15.5, df = 788, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.5350890 -0.4280595
sample estimates:
       cor 
-0.4833786 

save(ddi,file="../04-ProcessedData/ddi122023.RDS")

#BFNE

Consists of 12 items, range 1-5 Items 2, 4, 7, and 10 are reverse scored Forward= c(‘BFNE_1’, ‘BFNE_3’, ‘BFNE_5’, ‘BFNE_6’, ‘BFNE_8’, ‘BFNE_9’, ‘BFNE_11’, ‘BFNE_12’), Reverse= c(‘BFNE_2’, ‘BFNE_4’, ‘BFNE_7’, ‘BFNE_10’)

Trust respect scale

8 questions 1-strongly disagree; 7 - strongly agree 2,3,5,6 reverse-scored

trs %>% select(TRS_1:TRS_8) %>% gather() %>%  ggplot(aes(y=value,x=key,color=value))+geom_boxplot()+geom_jitter()

Warning: Removed 390 rows containing non-finite values (`stat_boxplot()`).
Warning: The following aesthetics were dropped during statistical transformation:
colour
ℹ This can happen when ggplot fails to infer the correct grouping structure in
  the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
  variable into a factor?
Warning: Removed 390 rows containing missing values (`geom_point()`).

Trust in Physician Scale

1 (strongly disagree)- 5 (strongly agree) 11 items, unweighted mean of response reverse scored - 1, 5, 7, 11 original scale alpha = .90

Summary score of medical skepticism was obtained by taking the unweighted mean of the four items.

tps<-mhhs_data %>% select(contains("tps"))

tps<-tps %>% mutate(across(c(TPS_1,TPS_5,TPS_7,TPS_11),~6-.)) %>% 
  rowwise() %>% mutate(TPS_Tot=sum(c_across(TPS_1:TPS_11))) %>% 
  ungroup()

#selecting the write-ins
tps %>% filter(TPS_O!="") %>% select(TPS_O)


tps_scoring<-tps %>% dplyr::select(TPS_1:TPS_11)
key_tps.list<-list(Total=c(1:11))
key_tps<-make.keys(11,key_tps.list,colnames(tps_scoring))

scales_tps<-scoreItems(key_tps.list,tps_scoring)
summary(scales_tps)

Call: scoreItems(keys = key_tps.list, items = tps_scoring)

Scale intercorrelations corrected for attenuation 
 raw correlations below the diagonal, (unstandardized) alpha on the diagonal 
 corrected correlations above the diagonal:
      Total
Total  0.89

tps %>% select(TPS_1:TPS_11) %>% gather() %>%  ggplot(aes(y=value,x=key,color=value))+geom_boxplot()+geom_jitter()

Warning: Removed 705 rows containing non-finite values (`stat_boxplot()`).
Warning: The following aesthetics were dropped during statistical transformation:
colour
ℹ This can happen when ggplot fails to infer the correct grouping structure in
  the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
  variable into a factor?
Warning: Removed 705 rows containing missing values (`geom_point()`).

Ethnic Identity Scale

reverse score 4,5,8

save(eis,eis_resp,eis_idfile="../04-ProcessedData/eis122023.RDS")

Error in save(eis, eis_resp, eis_idfile = "../04-ProcessedData/eis122023.RDS") : 
  object ‘../04-ProcessedData/eis122023.RDS’ not found

Everyday Iniquity - Content

bicf<-mhhs_data %>% select(contains("bicf"))

10 items 1=sd 5 =sa

The numbering is a little odd on the original scale. There’s only 10 items numbered to 14. 7 = 5 8 = 6 11 = 7 12 = 8 13 = 9 14 = 10 reverse score 7, 8, 11, 12, 13, 14 only for total subscale mean Harmony: 1, 2, 3, 6 Higher scores mean higher harmony (affective) Conflict: 7, 8, 11, 12, 13, 14 Items 2 and 8 are only asked for participants who self-identified as bicultural.

summary(scales_BICII)

Call: scoreItems(keys = key_BICII.list, items = BICII_scoring)

Scale intercorrelations corrected for attenuation 
 raw correlations below the diagonal, (unstandardized) alpha on the diagonal 
 corrected correlations above the diagonal:
      Total
Total  0.89

Tosca-3

Relationship assessment scale

Self-concealment

MINI-IPIP

Combining scales for projects